Search

A broadband waveguide slot filtenna array with low cross polarization and high efficiency is presented for dual-polarization applications. The vertical polarization radiation is achieved by offsetting longitudinal slots on the ridged waveguide, while the horizontal polarization radiation is achieved by V-shaped slots on the ridged waveguide. Two polarization ridged waveguide linear arrays combine to obtain dual-polarized radiation with cross-polarization levels under −43 dB in wide angles. To extend the bandwidth of the array, each $1 \times 16$ -element linear array is separated into two $1 \times 8$ -element arrays, which are fed by a two-way ridged waveguide divider via H-shaped slot couplings. The evanescent-mode ridged waveguide filters are integrated into a two-way ridged waveguide divider that suppresses the out-of-band signal interference. The signals of the interference bands are suppressed effectively, which are −25 dB lower than that of the operation band. Furthermore, 16 pairs of dual-polarized linear arrays are designed, fabricated, and measured. The results show that the array has perfect properties such as bandwidth, polarization purity, efficiency, and immunity to interference.

The different approaches used for target decomposition (TD) theory in radar polarimetry are reviewed and three main types of theorems are introduced: those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is proposed. To conduct the experiment, the polarimetric synthetic aperture radar (SAR) data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.